首页> 外文期刊>European journal of pharmaceutical sciences >Quantitative prediction of formulation-specific food effects and their population variability from in vitro data with the physiologically-based ADAM model: A case study using the BCS/BDDCS Class II drug nifedipine
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Quantitative prediction of formulation-specific food effects and their population variability from in vitro data with the physiologically-based ADAM model: A case study using the BCS/BDDCS Class II drug nifedipine

机译:基于生理学的ADAM模型从体外数据定量预测特定配方食品的功效及其种群变异性:使用BCS / BDDCS II类药物硝苯地平的案例研究

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Quantitative prediction of food effects (FE) upon drug pharmacokinetics, including population variability, in advance of human trials may help with trial design by optimising the number of subjects and sampling times when a clinical study is warranted or by negating the need for conduct of clinical studies. Classification and rule-based systems such as the BCS and BDDCS and statistical QSARs are widely used to anticipate the nature of FE in early drug development. However, their qualitative rather than quantitative nature makes them less appropriate for assessing the magnitude of FE. Moreover, these approaches are based upon drug properties alone and are not appropriate for estimating potential formulation-specific FE on modified or controlled release products. In contrast, physiologically-based mechanistic models can consider the scope and interplay of a range of physiological changes after food intake and, in combination with appropriate in vitro drug- and formulation-specific data, can make quantitative predictions of formulation-specific FE including the inter-individual variability of such effects. Herein the Advanced Dissolution, Absorption and Metabolism (ADAM) model is applied to the prediction of formulation-specific FE for BCS/BDDCS Class II drug and CYP3A4 substrate nifedipine using as far as possible only in vitro data. Predicted plasma concentration profiles of all three studied formulations under fasted and fed states are within 2-fold of clinically observed profiles. The % prediction error (%PE) in fed-to-fasted ratio of C_(max) and AUC were less than 5% for all formulations except for the C_(max) of Nifedicron (%PE = -29.6%). This successful case study should help to improve confidence in the use of mechanistic physiologically-based models coupled with in vitro data for the anticipation of FE in advance of in vivo studies. However, it is acknowledged that further studies with drugs/formulations exhibiting a wide range of properties are required to further validate this methodology.
机译:在进行人体试验之前,对药物对药代动力学的食品效应(FE)进行定量预测,包括人群变异性,可以通过优化需要进行临床研究的受试者人数和采样时间,或者通过消除进行临床试验的需要,来帮助进行试验设计。学习。 BCS和BDDCS等分类和基于规则的系统以及统计QSAR被广泛用于预测早期药物开发中FE的性质。但是,它们的定性而非定量性质使它们不太适合评估FE的大小。而且,这些方法仅基于药物特性,不适用于估计潜在的针对制剂或控释产品的特定配方FE。相比之下,基于生理的机理模型可以考虑食物摄入后一系列生理变化的范围和相互作用,并结合适当的体外药物和制剂特异性数据,可以对制剂特异性FE进行定量预测,包括这种影响的个体差异。在本文中,高级溶出,吸收和代谢(ADAM)模型应用于BCS / BDDCS II类药物和CYP3A4底物硝苯地平的配方特异性FE的预测,仅使用体外数据。在禁食和进食状态下,所有三种研究制剂的血浆浓度预测值均在临床观察值的2倍以内。除了硝苯地龙的C_(max)(%PE = -29.6%)外,所有配方的进食与空腹比C_(max)和AUC的预测误差%(%PE)小于5%。这项成功的案例研究应有助于提高对基于机械生理学模型的信心,并在体内研究之前将体外数据用于预测FE。然而,公认的是,需要对具有广泛特性的药物/制剂进行进一步研究,以进一步验证该方法。

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